Single Cell and Spatial Transcriptomics Defines the Cellular Architecture of the Antimicrobial Response Network in Human Leprosy Granulomas
Here we show the codes we used to make the plots in Figure 6
library(Seurat)
library(ggplot2)
library(gplots)##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowess# Load in the data
load("RR6_spatial-seq_seurat_object.Rda")
# Emulate ggplot default colors
gg_color_hue <- function(n) {
hues = seq(15, 375, length = n + 1)
hcl(h = hues, l = 65, c = 100)[1:n]
}
# Set the colors to use
my_palette = gg_color_hue(6)[c(2,5,1,6,3)]
my_palette = c(my_palette, "grey60")
# Plot Figure 6b
SpatialPlot(spatial_rr6, image.alpha=0, pt.size.factor=2, stroke=0, cols=my_palette) +
scale_y_reverse() + scale_x_reverse() + labs(fill=NULL) +
guides(fill=guide_legend(override.aes=list(size=5)))
# Plot Figure 6c
SpatialFeaturePlot(spatial_rr6, features="CYBB", alpha=c(1, 1), pt.size.factor=1.8, min.cutoff="q70",
max.cutoff="q90") + scale_fill_gradient(low="grey90", high=gg_color_hue(6)[6])## Scale for 'fill' is already present. Adding another scale for 'fill', which
## will replace the existing scale.
SpatialFeaturePlot(spatial_rr6, features="CCL5", alpha=c(1, 1), pt.size.factor=1.8, min.cutoff="q70",
max.cutoff="q90") + scale_fill_gradient(low="grey90", high=gg_color_hue(6)[5])## Scale for 'fill' is already present. Adding another scale for 'fill', which
## will replace the existing scale.
SpatialFeaturePlot(spatial_rr6, features="MMP2", alpha=c(1, 1), pt.size.factor=1.8, min.cutoff="q70",
max.cutoff="q90") + scale_fill_gradient(low="grey90", high=gg_color_hue(6)[3])## Scale for 'fill' is already present. Adding another scale for 'fill', which
## will replace the existing scale.
SpatialFeaturePlot(spatial_rr6, features="KLK5", alpha=c(1, 1), pt.size.factor=1.8, min.cutoff="q70",
max.cutoff="q90") + scale_fill_gradient(low="grey90", high=gg_color_hue(6)[1])## Scale for 'fill' is already present. Adding another scale for 'fill', which
## will replace the existing scale.
# Plot Figure 6d
# Average expression by cluster for RR6
ave_expr_rr6 = read.csv("RR6_ave_expr_by_cluster.csv", row.names=1)
colnames(ave_expr_rr6) = gsub("X", "", colnames(ave_expr_rr6))
colnames(ave_expr_rr6) = paste(colnames(ave_expr_rr6), "RR6", sep="-")
ave_expr_rr6 = ave_expr_rr6[,1:5]
ave_expr_rr6 = t(scale(t(ave_expr_rr6)))
# Average expression by cluster for RR7
ave_expr_rr7 = read.csv("RR7_ave_expr_by_cluster.csv", row.names=1)
colnames(ave_expr_rr7) = gsub("X", "", colnames(ave_expr_rr7))
colnames(ave_expr_rr7) = paste(colnames(ave_expr_rr7), "RR7", sep="-")
ave_expr_rr7 = ave_expr_rr7[,c(2,4,1,5,3)]
ave_expr_rr7 = t(scale(t(ave_expr_rr7)))
# Average expression by cluster for RR8
ave_expr_rr8 = read.csv("RR8_ave_expr_by_cluster.csv", row.names=1)
colnames(ave_expr_rr8) = gsub("X", "", colnames(ave_expr_rr8))
colnames(ave_expr_rr8) = paste(colnames(ave_expr_rr8), "RR8", sep="-")
ave_expr_rr8 = ave_expr_rr8[,c(3,5,1,6,2)]
ave_expr_rr8 = t(scale(t(ave_expr_rr8)))
# Average expression by cluster for T-lep1
ave_expr_tlep1 = read.csv("Tlep1_ave_expr_by_cluster.csv", row.names=1)
colnames(ave_expr_tlep1) = gsub("X", "", colnames(ave_expr_tlep1))
colnames(ave_expr_tlep1) = paste(colnames(ave_expr_tlep1), "T-lep1", sep="-")
ave_expr_tlep1 = ave_expr_tlep1[,1:5]
ave_expr_tlep1 = t(scale(t(ave_expr_tlep1)))
# Combine the four datasets
genes = c("APP","CCL14","CCL21","CD40LG","CXCL13","IL32","CCL5","GZMB","PRF1","GNLY","DEFB1",
"KRT6A","FLG2","KLK5","RNASE7","S100A7","CYBB","IDO1","MMP9","RNASE6","TXN",
"CCL18","CYP27B1","CCL3","LYZ","ADM","CXCL12","MMP2","CXCL2","CXCL3","PLA2G2A")
ave_expr_rr6 = ave_expr_rr6[genes,]
ave_expr_rr7 = ave_expr_rr7[genes,]
ave_expr_rr8 = ave_expr_rr8[genes,]
ave_expr_tlep1 = ave_expr_tlep1[genes,]
ave_expr = cbind(ave_expr_rr6, ave_expr_rr7, ave_expr_rr8, ave_expr_tlep1)
ave_expr = ave_expr[,c(1,6,11,16,2,7,12,17,3,8,13,18,4,9,14,19,5,10,15,20)]
colnames(ave_expr) = paste(rep(c("EC","TC","KC","ML","FB"), each=4), rep(c("RR6","RR7","RR8","T-lep1"), 5), sep=" ")
# Plot the heatmap
my_palette = colorRampPalette(c("blue","white","red"))(n=100)
heatmap.2(as.matrix(t(ave_expr)), Rowv=FALSE, Colv=FALSE, dendrogram="none", scale="none",
trace="none", key=TRUE, margins=c(10,10), adjCol=c(0.8,0.5),
col=my_palette, key.title=NA, key.ylab=NA, cexCol=1, density.info="none", sepcolor="grey60",
sepwidth=c(0.01,0.01), colsep=1:ncol(ave_expr), rowsep=1:nrow(ave_expr))
Session info
sessioninfo::session_info()## Registered S3 method overwritten by 'cli':
## method from
## print.boxx spatstat## Warning in system("timedatectl", intern = TRUE): running command 'timedatectl'
## had status 1## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.0 (2019-04-26)
## os Red Hat Enterprise Linux
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/Los_Angeles
## date 2021-03-09
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib source
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